A Napster for Financial Data? A Boon to Financial Planners and Individual Investors
Notice bibliographique
Résumé
Imagine this: any time of the day or night, and with just a few clicks of the mouse, a financial planner or an individual investor can access a company's present and past financial reports in extraordinary detail. In addition, an array of instant analyses of those data can be performed, which displays them either graphically or as conventional financial statements. Within seconds a user can compare a company's balance sheets with those of several competitors, examine an enterprise's debt-to-equity ratio in any fiscal period, chart its stock price history and download an audit client's major nonfinancial news. As if that's not enough, the same range of analyses can be performed for any public company worldwide and the information even can be converted into any currency. No way, you're probably thinking. At least not in my lifetime. Think again. All the underlying technology--XBRL (Extensible Business Reporting Language)--for it is available right now. All that's missing is a standardized protocol to implement it--and the first steps for creating such a protocol to perform those feats have been taken. A team comprising the Nasdaq Stock Market, Microsoft and PricewaterhouseCoopers just launched a pilot project designed to demonstrate that the concept of making both the data and the analysis tools available on the Internet is not only feasible but both practical and sought by financial professionals, investors and regulators. If you want to see what the future looks like, just point your Web browser to www.nasdaq.com/xbrl (see exhibit 1, below) and download a free demonstration file called the Excel Investor's Assistant. You'll need Excel 2000 or later to run the demo. [EXHIBIT 1 OMITTED] Once you're at the site, follow the instructions to install the pilot. When done, click on the file, Excel Investors Assistant.xls. You will be asked whether you want Excel to enable macros; you do, so click on the Enable Macros box (see exhibit 2, at right). [EXHIBIT 2 OMITTED] That will bring up the opening screen of Investor's Assistant (see exhibit 3, below). [EXHIBIT 3 OMITTED] The Investor's Assistant file contains two components: an Excel spreadsheet with built-in data analysis macros and formulas and a linked database that contains five years of financial information on 21 companies. The financial information, however, is not just raw data--that is, it's not just a compilation of financial numbers. Instead, each item in the database has been labeled with an XBRL tag that identifies the item as, for example, revenue, profit or short-range debt. The XBRL tags are based on standardized accounting definitions customized for various industries. (For more on XBRL, see Finally, Business Talks the Same Language, JofA, Aug.00, page 24.) A growing number of accounting software developers are incorporating XBRL into their products so a tag automatically gets attached to each item of financial information as it is entered and subsequently calculated by the accounting software. The tags eventually will be useful for anyone compiling both internal and external financial reports and tax returns. Because many industries have unique categories of financial data, an international consortium of more than 170 companies is preparing customized XBRL dictionaries, called taxonomies, that optimize the XBRL definitions so the tags can handle any special reporting structure. The goal is to make XBRL a fully universal financial information language that is both automatically attached to the data and transparent to the viewer. The XBRL International consortium was founded by the AICPA in 1999 and currently has active chapters in Australia, Canada, Germany, Japan, the United Kingdom, United States and Singapore. Chapters are being developed in Belgium, Hong Kong, India, Ireland, the Netherlands, New Zealand, South Africa, Spain, Sweden and Taiwan. …
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,116 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,007 | 0,014 |
| Communication savante | 0,030 | 0,103 |
| Science ouverte | 0,005 | 0,018 |
| Intégrité de la recherche | 0,014 | 0,029 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,144 | 0,101 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».